방법 비교

	지리정보시스템 기반 다기준 의사결정 분석 (GIS-MCDA)	로케이션-할당 모델	포아송 및 음이항 회귀분석
분야	공간분석	공간분석	계량경제학
계열	Process / pipeline	Process / pipeline	Regression model
기원 연도	2006	1963	1998
창시자	Jacek Malczewski (GIS-MCDA synthesis)	Leon Cooper; S. L. Hakimi	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
유형	Spatial multi-criteria suitability/decision analysis	Spatial facility-location optimization	Generalized linear model for count data
원전	Malczewski, J. (2006). GIS-based multicriteria decision analysis: a survey of the literature. International Journal of Geographical Information Science, 20(7), 703–726.	Cooper, L. (1963). Location-allocation problems. Operations Research, 11(3), 331–343.	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
별칭	GIS-MCDM, spatial multi-criteria analysis, GIS-AHP, weighted overlay suitability	facility location, p-median problem, maximal covering location problem, yer-tahsis modelleri	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
관련	4	4	4
요약	GIS-MCDA combines the map layers of a geographic information system with multi-criteria decision analysis to produce suitability or priority maps — ranking locations by how well they satisfy several weighted criteria at once. It is the standard framework for spatial decisions such as siting hospitals, solar farms, landfills, or evacuation areas, integrating methods like AHP, TOPSIS, and weighted overlay with spatial data.	Location-allocation models decide where to place a set of facilities and simultaneously assign demand points to them so as to optimize an objective such as total travel cost, worst-case distance, or population covered. Rooted in the operations-research work of Cooper (1963) and Hakimi (1964) and central to network GIS, they answer questions like where to site warehouses, hospitals, fire stations, or schools to best serve a spatially distributed population.	Poisson regression is a generalized linear model for count outcomes — events tallied as non-negative integers such as hospital admissions, accidents, or article counts. It models the log of the expected count as a linear function of the predictors, and is developed in the standard count-data treatment of Cameron and Trivedi (1998); when the counts are over-dispersed, the closely related negative binomial model (Hilbe, 2011) is preferred.